Calendar effects to forecast influenza seasonality: A case study in Milwaukee, WI

Influenza viral infection is contentious, has a short incubation period, yet preventable if multiple barriers are employed. At some extend school holidays and travel restrictions serve as a socially accepted control measure. A study of a spatiotemporal spread of influenza among school-aged children in Belgium illustrated that changes in mixing patterns are responsible for altering disease seasonality3.

June 18, 2019

Effective Collaboration Models for Statiscians and Public Health Departments

Public health departments need enhanced surveillance tools for population monitoring, and external researchers have expertise and methods to provide these tools. However, collaboration with potential solution developers and students in academia, industry, and government has not been sufficiently close or well informed for rapid progress. Many peer-reviewed papers on biosurveillance methods have been published by researchers, but few methods have been adopted in systems used by health departments. In a 2013 BioSense User Group survey with responses from users in more than 40 U.S.

October 05, 2017

Seasonal Synchronization of Influenza in the United States Older Adult Population

Elena Naumova, Department of Public Health and Community Medicine, Tufts University School of Medicine joined the August 2010 ISDS Literature Review to present her paper "Seasonal Synchronization of Influenza in the United States Older Adult Population" from PLoS One.


Elena Naumova, Department of Public Health and Community Medicine, Tufts University School of Medicine


Thursday, August 26, 2010


ISDS Research Committee

October 20, 2017

Enteric Disease Surveillance: Seasonal Changes in Population Profiles

In the last decade, time series analysis has become one of the most important tools of surveillance systems. Understanding the nature of temporal fluctuations is essential for successful development of outbreak detection algorithms, aberration assessment, and to control for seasonal variations. Typically, in applying the time series methods to health outcomes collected over an extended period of time it is assumed that population profiles remain constant. In practice, such assumptions have been rarely tested.

July 30, 2018

Seasonal Patterns of Respiratory Diseases: a Proxy for Influenza?

One of the most important goals of disease surveillance is to identify the "what" and "when" of an epidemic. Influenza surveillance is made difficult by inconsistent laboratory testing, deficiencies in testing techniques, and coding subjectivity in hospital records. We hypothesized that respiratory diseases other than influenza may serve as a useful proxy for this infection in pediatric populations, due to similarities in the seasonal characteristics of these illnesses.

July 30, 2018

What Happens in Vegas, Doesn’t Stay in Vegas: Traveling Waves of Influenza in the US Elderly Population, 1991-2004

Influenza is a significant public health problems in the US leading to over one million hospitalizations in the elderly population (age 65 and over) annually. While influenza preparedness is an important public health issue, previous research has not provided comprehensive analysis of season-by-season timing and geographic shift of influenza in the elderly population. These findings fail to document the intricacies of each unique influenza season, which would benefit influenza preparedness and intervention.

July 30, 2018

Accounting for Acceleration of Disease Rates with Age in Biosurveillance Systems: The SIMPLE Method

Accurate and precise estimation of disease rates for a given population during a specified time frame is a major concern for public health practitioners and researchers in biosurveillance. Many diseases follow distinct patterns; incidence and prevalence of many diseases increase approximately exponentially with age, including many cancers, respiratory infections, and gastroenteritis.

July 30, 2018

Infectious Outbreaks and Time-Distributed Effects of Exposure

The objective of this communication is to demonstrate an approach for modeling time-distributed effects of exposures to cases of infection which can be utilized in syndromic surveillance systems for characterizing, detecting, and forecasting a potential outbreak.

July 30, 2018

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